Why do moderately represented cultures show more flattening than data-poor cultures?

This explores why moderately-represented cultures get flattened harder than data-poor ones — and the corpus has a sharp mechanistic answer for *how* flattening works, even though the specific moderate-vs-poor comparison has to be assembled laterally rather than read off a single note. The anchor finding is that LLMs don't just produce flattened outputs — they internalize cultural flattening as an architectural pathway: low-resource cultures like Ethiopia and Algeria are *represented through* high-resource cultural proxies inside the model's internal states, not merely in what it says Do LLMs represent low-resource cultures through dominant cultural proxies?. Flattening is routing: a culture gets mapped onto its nearest dominant neighbor.

That routing picture suggests why the *amount* of data matters non-linearly. Work on ideological representation shows that depth of representation scales with feature richness — models can differ by up to 7.3× in how many distinct features they devote to a topic, and richer representations are both harder to steer and more internally consistent Can we measure how deeply models represent political ideology?. Read against the flattening result, a moderately-represented culture is exactly the case with *enough* signal for the model to confidently locate it — but not enough to give it its own dense feature set. So it gets interpolated onto the closest high-resource attractor and held there with confidence. A data-poor culture, by contrast, may be too sparse to confidently assimilate at all; the model has less to over-generalize from, so paradoxically it imposes less of a wrong-but-confident proxy.

The norm-prediction work sharpens the 'confidently wrong' part. Frontier models predict social appropriateness better than any individual human, yet *all of them share identical systematic errors* on unwritten norms Can AI systems learn social norms without embodied experience? Can AI learn social norms better than humans?. That's the signature of statistical pattern-matching that has mastered the dominant distribution and then applies it everywhere — competence at the center, identical blind spots at the margins. A related note makes the deeper point: models achieve top-percentile statistical performance while having no actual cultural participation or meaning-making Why do AI systems fail at social and cultural interpretation?. Flattening isn't a knowledge gap the model knows it has; it's a confident projection from the center outward.

And the cost lands hardest because users don't catch it. Across every language tested, people track an AI's *confidence* signals rather than its accuracy, and systematically over-rely on confident outputs even when wrong Do users worldwide trust confident AI outputs even when wrong?. A moderately-represented culture that gets fluently but wrongly rendered through a dominant proxy produces exactly the kind of confident, plausible output that users won't flag — whereas a data-poor culture more likely triggers visible hedging or refusal. The thing you didn't know you wanted to know: flattening may be worst not where the model knows least, but where it knows *just enough to stop asking*.